A Theoretical Analysis of Immune Inspired Somatic Contiguous Hypermutations for Function Optimization

  • Thomas Jansen
  • Christine Zarges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5666)


Artificial immune systems can be applied to a variety of very different tasks including classical function optimization. There are even artificial immune systems tailored specifically for this task. In spite of the successful application there is little knowledge and hardly any theoretical investigation about how and why they perform well. Here a rigorous analysis for a specific type of mutation operator introduced for function optimization called somatic contiguous hypermutation is presented. While there are serious limitations to the performance of this operator even for simple optimization tasks it is proven that for some types of optimization problems it performs much better than standard bit mutations most often used in evolutionary algorithms.


Optimization Time Mutation Operator Markov Chain Model Artificial Immune System Random Initialization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas Jansen
    • 1
  • Christine Zarges
    • 2
  1. 1.Department of Computer ScienceUniversity College CorkCorkIreland
  2. 2.Fakultät für Informatik, LS 2TU DortmundDortmundGermany

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